Optimal Control Research Articles

Chaotic dynamics and optimal therapeutic strategies for Caputo fractional tumor immune model in combination therapy.

In this paper, a Caputo fractional tumor immune model of combination therapy is established. First, the stability and biological significance of each equilibrium point are analyzed, and it is demonstrated that chaos may arise under specific conditions. Combined with the mathematical definition of Caputo fractional differentiation (CFD), it is found that there is a high correlation between the chaotic phenomenon of the patient's condition and the sensitivity of the patient to the change in the state of the day. The bifurcation threshold of each parameter is determined through numerical simulation, and the Hopf bifurcation of direct competition coefficient and inhibition coefficient between tumor cells and host healthy cells is elaborated upon in detail. Subsequently, a novel method combining optimal control theory with the particle swarm optimization (PSO) algorithm is proposed for the optimal control of the tumor immune model in combination therapy. Finally, the Adams-Bashforth-Moulton (ABM) prediction correction method is utilized in numerical simulations which demonstrate that the introduction of the CFD alters the model dynamics. Furthermore, these results indicate that fractional calculus can effectively be applied to tumor immune models better to elucidate complex chaotic dynamics of tumor cell evolution. Concurrently, the PSO can be successfully integrated with optimal control theory to address optimization challenges in cancer treatment.

Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators

In this paper, for addressing the safe control problem of modular robot manipulators (MRMs) system with uncertain disturbances, an approximate optimal control scheme of nonzero-sum (NZS) differential games is proposed based on the control barrier function (CBF). The dynamic model of the manipulator system integrates joint subsystems through the utilization of joint torque feedback (JTF) technique, incorporating interconnected dynamic coupling (IDC) effects. By integrating the cost functions relevant to each player with the CBF, the evolution of system states is ensured to remain within the safe region. Subsequently, the optimal tracking control problem for the MRM system is reformulated as an NZS game involving multiple joint subsystems. Based on the adaptive dynamic programming (ADP) algorithm, a cost function approximator for solving Hamilton–Jacobi (HJ) equation using only critic neural networks (NN) is established, which promotes the feasible derivation of the approximate optimal control strategy. The Lyapunov theory is utilized to demonstrate that the tracking error is uniformly ultimately bounded (UUB). Utilizing the CBF’s state constraint mechanism prevents the robot from deviating from the safe region, and the application of the NZS game approach ensures that the subsystems of the MRM reach a Nash equilibrium. The proposed control method effectively addresses the problem of safe and approximate optimal control of MRM system under uncertain disturbances. Finally, the effectiveness and superiority of the proposed method are verified through simulations and experiments.

Optimal Trajectory-Based Control of 3-D Dual Rotary Cranes for Payload Dynamic Regulation in Complex Environments

Optimal Trajectory-Based Control of 3-D Dual Rotary Cranes for Payload Dynamic Regulation in Complex Environments

Event-triggered explorized IRL-based decentralized fault-tolerant guaranteed cost control for interconnected systems against actuator failures

This paper presents a novel data-based decentralized guaranteed cost (DGC) fault tolerant control (FTC) scheme for the large-scale systems subject to actuator faults and mismatched interconnection. First, the FTC issues of interconnected systems are converted into a series of near optimal event-triggered control (ETC) methods for isolated subsystems via constructing a modified performance index function of each subsystem. By means of adaptive dynamic programming (ADP) algorithm, the upper bound of performance index function of large-scale systems can be obtained by solving the Hamilton–Jacobi-Bellman (HJB) equation of each auxiliary subsystem. Second, according to the proposed ADP-based decentralized approach and utilizing the event-based synchronous integral reinforcement learning (IRL) algorithm, a model-free guaranteed cost (GC) FTC approach is developed for interconnected large-scale system which can relax the restriction on the condition that system functions must be known. Further, the ultimate uniformly bounded (UUB) stability of auxiliary subsystems can be proved according to the Lyapunov principle. Finally, the effectiveness of the proposed control method is verified by presenting the simulation results.

A qualitative study of type 1 diabetes complications, mental health, and structural pathways of complications occurrence among young people (14-24 years) and caregivers in southern Ghana

BackgroundYoung people (aged 14-24 years) living with Type 1 Diabetes (T1D), especially, those in low-resource settings risks having T1D complications, and poor mental health. Yet, little is known about T1D complications, mental health, and their lived experiences among young people and their caregivers in Ghana. ObjectiveThis article sought to understand a) the dynamics of T1D complications, mental health, and lived experiences, and b) drawing on this evidence, develop structural and behavioural pathways of T1D complication occurrence among young people in Ghana. MethodsThe study design was interpretivist phenomenology (qualitative). Twenty-eight young people living with T1D (YPLWD), 12 caregivers, and six healthcare providers across three regions in southern Ghana were purposively recruited and interviewed using interview guides. The methods included a photovoice where the YPLWD took photographs to highlight visible complications. Themes were formed from the quotes and photovoice. The analytical technique was thematic; performed with the aid of QSR NVivo 14. ResultsFour main themes which were physical complications, mental health, other adverse health outcomes, and structural pathways of complication occurrence were identified from the analyses. Physical complications discussed included visual impairment, diabetic coma, reproductive health, and kidney disease. We found on the average, eight (8) years of living with T1D (with minimum and maximum years of 2 and 4 respectively). Mental health issues including anxiety and suicidal ideation transcended YPLWD to caregivers. Multiple structural and behavioural risk factors of T1D complications were also found. ConclusionThe YPLWD and caregivers in Ghana experienced multiple T1D complications and poor mental health. Unfortunately, these manifested early in their natural history of T1D, and were influenced by inadequate structural resources and adoption of sub-optimal self-care practices. This article underscores the need to improve structural resources, promote optimal blood glucose control, alongside mental/psychosocial support for YPLWD and their caregivers in Ghana.

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25% to 99.13% across 10 test videos and an average accuracy of 97.88%. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance.

Pengaruh Penyesuaian Parameter Membership Function pada Sistem Kendali Robot Balancing Berbasis Fuzzy Logic

This research develops a balancing robot control system using a fuzzy logic approach, focusing on the adjustment of membership function parameters. The main components of the system include the ESP32 microcontroller, MPU6050 sensor for detecting tilt angle and angular velocity, and L298 motor driver for DC motor actuation. Triangular-shaped membership functions are implemented, and parameters a, b, and c are adjusted through simulation to enhance system performance. Evaluation results indicate an average settling time of 1.2 seconds, a maximum overshoot of 5%, and a steady-state error of less than 2 degrees. This adjustment successfully balances response speed and stability, providing important guidance for developers in designing a more optimal fuzzy logic control system. The research was conducted at the Electrical Engineering Laboratory of 17 August 1945 University (Untag) Surabaya from June to December 2023.

Optimal Control Strategy for a Cutting-Edge Hybrid Ventilation System in Classrooms: Comparative Analysis Based on Air Pollution Levels Across Cities

Optimal Control Strategy for a Cutting-Edge Hybrid Ventilation System in Classrooms: Comparative Analysis Based on Air Pollution Levels Across Cities

Flexible heat and power load control of subcritical heating units based on energy demand-supply balance

To fulfill the requirement for enhancing the flexibility of heating units within the framework of renewable energy grid integration. In this study, a proposed optimal control strategy for heating units borrows the heat extraction output to perform rapid load changes; the boiler heat supply within the unit is clearly differentiated from the turbine energy demand, and formulate corresponding control signals. The coordinated control of the boiler and turbine based on the energy demand-supply balance of the unit as secondary control for the final load accuracy, ensuring its safe and stable operation. To further enhance the control performance, an improved sparrow optimization algorithm with butterfly search is designed to adjust the controller parameters. Finally, field tests on a 300MW subcritical heating unit reveal that the automatic generation control performance index is improved by 32.16% compared to the traditional coordinated control strategy, while taking less impact on the heat user. These results validate the exceptional performance and significant practical value of the optimized control strategy.

Optimization control of time-varying cyber–physical systems via dynamic-triggered strategies

Optimization control of time-varying cyber–physical systems via dynamic-triggered strategies

Data-enhanced convolutional network based on air conditioning system start/stop time prediction

Most enterprise workshop operators frequently adjust the start/stop time of air conditioning systems based on indoor and outdoor temperatures and humidity to accommodate changing demand and weather conditions. However, relying on personal subjective experience for these adjustments often leads to operational delays or energy waste due to the lack of precision in determining optimal timing. Predicting air conditioning system start and stop times is crucial for energy consumption and savings in HVAC systems. Traditional data-driven methods have been insufficient in this regard, as they mainly focus on feature mapping and overlook the dynamic coupling relationships of process variables, resulting in subpar predictions. In response to this challenge, the paper introduces a novel approach known as the Periodicity and Long-Term Convolutional Neural Network (PLCNN). This method converts one-dimensional regression prediction data into two-dimensional data containing time series features to capture the dynamic coupling characteristics of the air conditioning system while maintaining the independent variation relationships of features. Experimental results using real factory floor data have demonstrated the superior performance of the PLCNN method. Specifically, this method achieved a 14.96% lower error rate compared to the traditional method and an 8.18% improvement compared to the deep learning method. Moreover, the implementation of the PLCNN method in the optimal control of air conditioning systems led to a significant 19.43% reduction in total monthly energy consumption. In conclusion, the proposed method offers a promising alternative to traditional approaches to forecasting and provides a solution to the common challenges encountered in traditional prediction tasks.

Vaccination strategies in a pair formation model for human papillomavirus infection: An optimal control approach

Vaccination strategies in a pair formation model for human papillomavirus infection: An optimal control approach

Vegetation restoration strategies in arid or semi-arid regions-From the perspective of optimal control.

Inappropriate human activities contribute to the degradation of ecosystems in arid or semi-arid regions. Therefore, emphasizing the importance of strategies for restoring vegetation in these areas cannot be overstated. However, there has been insufficient research on how to develop effective restoration strategies at minimal cost. This paper addresses this gap by studying how optimizing the spatiotemporal distribution of human activities through local and boundary controls can reduce the level of desertification in vegetation pattern structures, thereby facilitating the recovery of arid land vegetation. The results indicate that vegetation restoration depends on the proportion and number of human activity areas, with a trade-off between them. Furthermore, consistent conclusions were obtained on circular regions, demonstrating the robustness of the approach to boundary shapes. This paper aims to offer new insights into the restoration of arid land vegetation and the prevention of catastrophic ecosystem changes from the perspective of optimal control.

Dynamic pricing and inventory model for perishable products with reference price and reference quality

Reference price (quality) is a benchmark point used by consumers to make price (quality) judgements. Combining reference price and reference quality with dynamic pricing and inventory management, this paper applies differential equations theory to construct a optimal control model for perishable products when the quality of products declines exponentially. The demand of products depends on price, quality, reference price and reference quality, among which reference price and reference quality are influenced by consumers’ past memory. The aim is to maximize the retailer’s profit during the period. The conclusions are as follows. Firstly, a optimal dynamic pricing and inventory model for perishable products with reference price and reference quality is constructed, and the model is extended to dual decision variables, stochastic demand, time-dependent effects, competitive and infinite planing horizon setups. Secondly, through the Pontryagin maximum principle, the analytical expression of the optimal dynamic price is derived. Thirdly, a linear search method to solve the optimal static price is proposed. Finally, the sensitivity of the main parameters is analyzed and the corresponding management enlightenments are given. By comparison of dynamic and static pricing, we find that dynamic pricing can achieve more profits and take a shorter selling period. In addition, for the sales problem of perishable products affected by both reference price and reference quality, retailers should adopt skimming pricing strategy (the optimal price decreases with time). Furthermore, to obtain more profit, retails should strive to increase the sensitivity coefficients of two types of reference, and the reference quality memory coefficient, while to decrease the reference price memory coefficient.

Research on the application of a hybrid intelligent algorithm in reducing the negative response of shield tunneling construction

Traditional shield project control relies on manual monitoring and management, which suffers from poor reliability, lag, and other defects. In this paper, a hybrid algorithm based on Bayesian optimization (BO), light gradient enhancer (LGBM), and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to realize intelligent control of shield construction safety. The BO-LGBM-NSGA-III is a multiobjective optimization algorithm that takes cutterhead wear and shield energy consumption as objective functions and the nonlinear mapping function of surface settlement and construction parameters as fitness functions. Based on the Pareto distribution of the optimized backing structure construction parameters, reasonable limits of the control parameters of the shield tunneling machine are obtained. The conclusions are as follows. (1) The R2 of the test set for surface subsidence prediction is 0.916, and the RMSE is 2.655. (2) The proposed method can achieve multiobjective optimization, and the surface settlement, cutter head wear, and shield energy consumption are optimized by 49.92%, 24.38%, and 41.88%, respectively. (3) By using this method, both prediction and optimization can be realized, and the optimal control scheme can be obtained. The negative impact of shield tunneling construction has been significantly reduced.

Research on trajectory planning based on deep reinforcement learning

Abstract For trajectory planning in a three-dimensional continuous space with multiple complex constraints, traditional optimal control methods face significant challenges, such as long computation times and poor dynamic performance. Using deep reinforcement learning algorithms for training agents in flight trajectory planning can effectively improve the dynamic performance of the planning algorithms. This paper focuses on training agents for UAV emergency landing and obstacle avoidance trajectory planning based on deep reinforcement learning algorithms. Within the reinforcement learning framework, we design the training environment, state space, action space, reward function, and neural network structure. To address common issues in reinforcement learning, such as poor generalization and slow training speed, we improve the training process using the concept of curriculum learning. Comparisons with pseudospectral methods demonstrate that deep reinforcement learning can effectively handle trajectory planning tasks in real flight environments, achieving trajectory quality close to that of pseudospectral methods while offering superior real-time performance.

Optimization of batch cooling crystallization systems considering crystal growth, nucleation and dissolution. Part I: Simulation

Optimal control of batch crystallization systems is still a focus and hot topic in the field of industrial crystallization, which seriously affects the consistency of batch product quality. In this paper, a new method with a new objective function and improved optimization algorithm was proposed for optimization of crystal size distribution (CSD) in case of fine crystals occurrence. The new objective function was developed with better margin metric and weighting technique to minimize fine crystal mass, meanwhile, a newly constructed sinusoidal weight function was introduced to improve the particle swarm optimization (PSO) algorithm. A precise control of CSD with suppressed numerical discrepancy caused by fine crystals removal was developed by combining seed recipe and temperature-swing. In addition, the effects of temperature curve segments on CSD during process optimization were systematically investigated to achieve optimal results. Two typical batch cooling crystallization systems were used to verify the effectiveness of the proposed method in controlling product CSD while minimizing fine crystal mass. Results demonstrated that the desired product CSD can be achieved with minor errors while the fine crystals could be shrunk to be negligible, i.e., the fine crystal mass and number can be reduced by over 90%. This work has an important guiding significance for the removal of fine crystals in industrial crystallization processes, especially when only operational optimization rather than equipment updating is considered.

Indefinite Linear-Quadratic Optimal Control of Mean-Field Stochastic Differential Equation With Jump Diffusion: An Equivalent Cost Functional Method

Indefinite Linear-Quadratic Optimal Control of Mean-Field Stochastic Differential Equation With Jump Diffusion: An Equivalent Cost Functional Method

Maximizing the net economic benefits of regulating indoor air quality in China

The WHO guideline levels for indoor air pollutants (IAPs) are based solely on health considerations. Attaining these levels may cost a disproportionate fraction of available public heath funds. To maximize the net economic benefit (NEB, = benefits - costs) of controlling IAPs, we develop a method based upon a new concept, optimal control concentration (OCC). For China, the estimated OCCs for indoor PM2.5 and formaldehyde in residences are 15 μg/m3 and 25 μg/m3, respectively. The estimated NEB achieved by controlling IAPs to their OCCs is almost 500 billion CNY a year, much greater than that of meeting the WHO guidelines (a negative NEB) and two Chinese standards (a negative NEB and about 200 billion CNY). Our method can be used by other countries to cost-effectively control IAPs.

Distributed Optimal Control of Energy Storages in DC Microgrids for Operation Loss Minimization With Communication Delays

Distributed Optimal Control of Energy Storages in DC Microgrids for Operation Loss Minimization With Communication Delays